GAN-Based Video Shot Matching Using Clustering and Discriminator Feedback
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Solution Overview
Problem
Prior art video editing applications perform context-insensitive shot-matching, which can distort images with extreme color distribution, and require manually generated color-matched image pairs for training convolutional neural networks, making the process time-consuming and expensive.
Innovation Solution
A generative adversarial network (GAN) is trained using automatically extracted shot-matched image pairs to predict color parameter modifications for context-sensitive shot-matching, where the GAN includes a generator model to modify source images and a discriminator model to determine shot-matching likelihood, using a set of image pairs clustered by color features and content similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context-insensitive shot-matching is used, then the shot-matching process is simple and fast, but the accuracy deteriorates due to distortion of images with extreme color distribution
Solution Approach 1:
The system performs preliminary actions by extracting and clustering color features from images before the actual shot-matching process. Image frames are clustered based on color histograms, and representative color patches are pre-selected from each cluster. This preparation enables the neural network to focus on semantically relevant regions during inference, improving accuracy without significantly increasing real-time processing time.
Solution Approach 2:
The invention applies local quality by differentiating between various regions of images based on their semantic content and color distribution characteristics. Instead of treating all pixels uniformly, the system identifies and weights different regions (e.g., skin tones, objects, backgrounds) differently during the shot-matching process, allowing context-sensitive adjustments that improve accuracy while maintaining efficiency.
2Measurement precision
If manually generated color-matched image pairs are used for training CNN models, then the training data quality is high, but the time and expense increase significantly
Solution Approach 1:
The system performs self-service by automatically generating its own training data through computational methods. Instead of relying on manual annotation by professional colorists, the system uses neural networks to automatically extract color features, cluster images, and generate synthetic color-matched pairs. This self-service approach maintains high training data quality while dramatically reducing the time and human resources required.
Solution Approach 2:
The invention uses copying by creating synthetic copies of color-matched image pairs through computational generation. The system generates artificial training data that replicates the quality of manually color-matched pairs by using neural networks to predict and synthesize appropriate color transformations. These synthetic copies serve as training data without requiring actual manual editing.
3Ease of manufacture
If CNN models are trained with automatically extracted image pairs, then the data generation cost is reduced, but the shot-matching context sensitivity deteriorates
Solution Approach 1:
The system performs preliminary semantic analysis and clustering of image features before generating training pairs. By pre-organizing images based on their semantic content and color characteristics, the system ensures that automatically generated pairs maintain contextual relevance. This preliminary structuring of data enables the neural network to learn context-sensitive relationships even from automatically generated training material.
Data Source
AI summary
This disclosure involves training generative adversarial networks to shot-match two unmatched images in a context-sensitive manner. For example, aspects of the present disclosure include accessing a trained generative adversarial network including a trained generator model and a trained discriminator model. A source image and a reference image may be inputted into the generator model to generate a modified source image. The modified source image and the reference image may be inputted into the discriminator model to determine a likelihood that the modified source image is color-matched with the reference image. The modified source image may be outputted as a shot-match with the reference image in response to determining, using the discriminator model, that the modified source image and the reference image are color-matched.